ColdDDI: Evaluating Knowledge Utilization in Cold-Start Drug-Drug Interaction Prediction

Jiheng Liang; Chen Zhao; Di Wu; Chenyang Bu; Yunpeng Hong; Xingquan Zhu; Yi He

cs.LGOct 4, 2026

ColdDDI: Evaluating Knowledge Utilization in Cold-Start Drug-Drug Interaction Prediction

Authors: Jiheng Liang, Chen Zhao, Di Wu, Chenyang Bu, Yunpeng Hong, Xingquan Zhu, Yi He

Organizations: Data Science, William & Mary · Computer Science, Baylor University · Southwest University · Hefei University of Technology · Florida Atlantic University

Abstract

Cold-start drug-drug interaction (DDI) prediction tests whether models can identify clinically significant interactions for drugs without training-time interaction history. Existing benchmarks mostly report aggregate edge-prediction scores, leaving a key evaluation question unanswered: when models receive molecular, textual, or knowledge-graph (KG) evidence, do they actually use the evidence that pharmacologically supports the interaction? We introduce ColdDDI, a reconstructible diagnostic benchmark built from DrugBank 5.1.13, with 1,900 approved small-molecule drugs and 565,731 positive DDI pairs. ColdDDI evaluates pairs with zero, one, or two unseen drugs. It also annotates each interaction by whether it changes drug exposure or drug effect, and by whether the biomedical knowledge graph contains shared enzymes, transporters, or targets that can plausibly mediate the interaction. These annotations separate evidence availability from predictive dependence. We evaluate eight conventional DDI methods and 13 LLMs; for open-weight LLMs, we test five prompt patterns and use masking, drug replacement, and channel-sensitivity metrics to probe knowledge utilization. ColdDDI exposes that, in the hardest split where both drugs are unseen, the main performance divide is mediator availability. A fine-tuned 1B LLM recovers 89-93% of interactions with a shared enzyme, transporter, or target, but only 40-62% without such a mediator. More importantly, KG-provided evidence is not always used; several KG-augmented baselines change little when the shared mediator is masked or disrupted, whereas fine-tuned LLMs respond strongly to this intervention. Thus, ColdDDI evaluates knowledge utilization rather than knowledge access alone, showing where cold-start DDI models rely on mechanistic evidence and where they fail despite receiving it. Code is available at https://github.com/0217ljh/ColdDDI-NeurIPS2026.

Figures & tables

Benchmark	Year	Source	Scale	D1	D2	D3	D4
			(drugs / pairs)	Setting	Mechanism	Modality	Model
TWOSIDES [ 22 , 23 ]	2012	FAERS / SIDER	645 / 63K	✗	✗	✗	✗
DeepDDI [ 1 ]	2018	DrugBank 5.0	1,710 / 192K	✗	✗	✗	✗
OGBL-DDI [ 24 ]	2020	DrugBank 5.0	4,267 / 1.3M †	✗	✗	✗	✗
TDC-DDI [ 25 ]	2021	DrugBank 5.0	1,706 / 192K	✗	✗	✗	✗
DDInter [ 26 ]	2022	FDA / Lit.	1,972 / 237K	✗	$\boldsymbol{\sim}$	✗	✗

Table 1 : Comparison of DDI evaluation resources. ✓ fully /

\boldsymbol{\sim}

partial / ✗ not supported. D1 Setting : drug-wise S0/S1/S2 splits. D2 Mechanism : pharmacological mechanism and KG-mediated stratification. D3 Modality : input/prompt comparison with per-modality diagnostics. D4 Model : diverse traditional models and LLM families.

	S0 (Transductive)	S1 (Semi-Inductive)	S2 (Fully Inductive)
Train (+)	$\mathcal{E}_{\text{train}}\subseteq\mathcal{E}_{\text{all}}^{(G_{1})}$
Train ( $-$ )	$\mathcal{N}_{\text{train}}^{(t)}\sim\overline{\mathcal{E}}^{(G_{1})}$ , resampled each epoch
Val/Test (+)	$\mathcal{E}_{\text{all}}^{(G_{1})}\setminus\mathcal{E}_{\text{train}}$	$\{(u,v)\mid u\in G_{1},v\in G_{2}\}\cap\mathcal{E}_{\text{all}}$	$\mathcal{E}_{\text{all}}^{(G_{2})}$
Val/Test ( $-$ )	fixed from $\overline{\mathcal{E}}^{(G_{1})}$	fixed from $\overline{\mathcal{E}}^{(G_{1},G_{2})}$	fixed from $\overline{\mathcal{E}}^{(G_{2})}$

Table 2 : Split composition for the three evaluation settings.

G_{1}

: seen drugs;

G_{2}

: unseen drugs;

G_{1}\cap G_{2}=\emptyset

. Negatives are sampled from the corresponding

\overline{\mathcal{E}}

spaces.

		AUC-ROC			Recall
Group	Method	S0	S1	S2	S0	S1	S2
Matrix	DeepDDI [ 1 ]	$0.997_{\pm 0.000}$	$0.819_{\pm 0.003}$	$0.672_{\pm 0.009}$	$0.964_{\pm 0.001}$	$0.682_{\pm 0.012}$	$0.509_{\pm 0.030}$
Mol-Graph	SSI-DDI [ 7 ]	$0.829_{\pm 0.010}$	$0.706_{\pm 0.006}$	$0.627_{\pm 0.003}$	$0.771_{\pm 0.007}$	$0.628_{\pm 0.015}$	$0.580_{\pm 0.040}$
	DSN-DDI [ 8 ] †	$0.950_{\pm 0.038}$	$\mathbf{0.827}_{\pm 0.056}$	$0.711_{\pm 0.063}$	$0.936_{\pm 0.028}$	$\mathbf{0.731}_{\pm 0.038}$	$0.696_{\pm 0.095}$
	HDN-DDI [ 9 ]	$0.853_{\pm 0.006}$	$0.736_{\pm 0.003}$	$0.645_{\pm 0.002}$	$0.789_{\pm 0.026}$	$0.659_{\pm 0.004}$	$0.589_{\pm 0.036}$
KG-only	EmerGNN [ 14 ]	$0.980_{\pm 0.001}$	$0.799_{\pm 0.000}$	$0.707_{\pm 0.006}$	$0.939_{\pm 0.009}$	$0.712_{\pm 0.003}$	$0.619_{\pm 0.014}$

Table 3 : AUC-ROC and Recall on the 1,900-drug set (3-seed mean

\pm

std). Best in bold .

Method	PK-A		PK-B		PD-A		PD-B		A–B gap
Method	Recall	AUC-ROC	Recall	AUC-ROC	Recall	AUC-ROC	Recall	AUC-ROC	Recall	AUC-ROC
DeepDDI	$0.515_{\pm 0.038}$	$0.678_{\pm 0.034}$	$0.370_{\pm 0.012}$	$0.578_{\pm 0.055}$	$0.614_{\pm 0.021}$	$0.739_{\pm 0.024}$	$0.508_{\pm 0.092}$	$0.675_{\pm 0.036}$	$+0.125_{\pm 0.053}$	$+0.082_{\pm 0.024}$
SSI-DDI	$0.558_{\pm 0.201}$	$0.642_{\pm 0.081}$	$0.349_{\pm 0.129}$	$0.501_{\pm 0.052}$	$0.692_{\pm 0.069}$	$0.742_{\pm 0.073}$	$0.538_{\pm 0.047}$	$0.634_{\pm 0.038}$	$+0.182_{\pm 0.031}$	$+0.125_{\pm 0.033}$
DSN-DDI	$0.533_{\pm 0.014}$	$0.767_{\pm 0.054}$	$0.465_{\pm 0.041}$	$\boldsymbol{0.719_{\pm 0.073}}$	$0.608_{\pm 0.111}$	$0.789_{\pm 0.039}$	$0.541_{\pm 0.022}$	$\boldsymbol{0.768_{\pm 0.042}}$	$+0.067_{\pm 0.063}$	$+0.034_{\pm 0.030}$
HDN-DDI	$0.734_{\pm 0.052}$	$0.698_{\pm 0.049}$	$0.440_{\pm 0.147}$	$0.507_{\pm 0.055}$	$0.762_{\pm 0.042}$	$0.741_{\pm 0.035}$	$0.626_{\pm 0.087}$	$0.630_{\pm 0.022}$	$+0.215_{\pm 0.077}$	$+0.151_{\pm 0.012}$
EmerGNN	$0.777_{\pm 0.037}$	$0.851_{\pm 0.022}$	$0.308_{\pm 0.039}$	$0.565_{\pm 0.047}$	$0.743_{\pm 0.086}$	$0.828_{\pm 0.053}$	$0.436_{\pm 0.109}$	$0.660_{\pm 0.046}$	$+0.388_{\pm 0.078}$	$+0.227_{\pm 0.031}$

Table 4 : S2 Recall and AUC-ROC by mechanism subtype on the 800-drug subset (3-seed mean

\pm

std). Best in bold .

Group	Entity mask	Name mask	Both mask	KSAI	Dominant
	(R0 $\to$ R2)	(R0 $\to$ R1)	(R0 $\to$ R3)		channel
PK-A	$\boldsymbol{-8.0_{\pm 2.7}}$	$+1.5_{\pm 1.5}$	$-7.0_{\pm 1.9}$	$+1.2_{\pm 1.0}$	Entity
PK-B	$+0.4_{\pm 1.8}$	$\boldsymbol{-4.4_{\pm 0.4}}$	$-4.9_{\pm 2.4}$	$+3.3_{\pm 0.5}$	Name
PD-A	$\boldsymbol{-2.9_{\pm 2.5}}$	$-0.6_{\pm 1.0}$	$-5.5_{\pm 3.9}$	$+3.8_{\pm 0.8}$	Entity
PD-B	$-1.6_{\pm 0.4}$	$\boldsymbol{-3.4_{\pm 1.2}}$	$-7.5_{\pm 2.6}$	$+4.6_{\pm 0.4}$	Name
ALL	$-3.6_{\pm 1.0}$	$-1.7_{\pm 0.8}$	$-6.8_{\pm 0.4}$	$+3.1_{\pm 0.4}$

Table 5 : Masking effects and cross-channel asymmetry on LLM-FT (

\times 10^{2}

, 800-drug subset, 3-seed mean

\pm

std). Columns 2–4 are

\Delta

AUC-ROC vs. R0 per mask condition. KSAI is the per-pair averaged

|P_{R_{1}}{-}P_{R_{3}}|{-}|P_{R_{0}}{-}P_{R_{2}}|

defined in Appendix E.1 . Bold marks the dominant channel per row.

Group	Method	Absolute KPS-F	A–B gap
Matrix	DeepDDI	$0.317_{\pm 0.066}$	$+0.052_{\pm 0.030}$
Mol-Graph	SSI-DDI	$0.164_{\pm 0.056}$	$+0.022_{\pm 0.014}$
	DSN-DDI	$0.412_{\pm 0.065}$	$+0.035_{\pm 0.021}$
	HDN-DDI	$0.154_{\pm 0.043}$	$+0.032_{\pm 0.010}$
KG-only	EmerGNN	$0.388_{\pm 0.016}$	$+0.170_{\pm 0.057}$
KG-integrated	TIGER	$0.216_{\pm 0.074}$	$+0.008_{\pm 0.018}$

Table 6 : KPS-F and A–B gap by method class ( Section 4 ), 3-seed mean

\pm

std on 800-drug subset, S2.

Intervention	PK-A	PK-B	PD-A	PD-B
Zero KG attention	$\boldsymbol{-0.219}$	$-0.061$	$-0.191$	$-0.081$
Zero name attention	$+0.000$	$-0.040$	$-0.002$	$\boldsymbol{-0.036}$

Table 7 : Attention intervention on LLM-FT (Llama-3.2-1B, S2, single random seed). Attention from the answer token to KG-entity (resp. name) tokens is zeroed across every layer and head.

\Delta

AUROC vs. unintervened baseline.

Llama			Qwen 2.5			Gemma 3			Closed-source
Size	Inf	FT	Size	Inf	FT	Size	Inf	FT	Model	Inf
3.2-1B	0.525	0.776	0.5B	0.489	0.766	1B	0.520	0.756	GPT-4o	0.710
3.2-3B	0.459	0.785	3B	0.517	0.769	4B	0.496	0.781	Claude Sonnet 4.6	0.751
2-7B	0.474	0.731	7B	0.486	0.740	12B	0.548	0.798
2-13B	0.513	0.781	14B	0.510	0.750

Table 8 : S2 AUC for LLMs (P4, 800-drug subset). FT is 3-seed mean, Inf single-seed. Bold marks per-family best (combined AUC-ROC and Recall, Appendix D.4 ). Closed-source metrics in Table 40 .

Appendix figures & tables67 assets

Supplementary material from the paper’s appendix.

Appendix

Step	Operation	Drugs	DDI Edges	DDI Types
0	DrugBank 5.1.13 (raw)	17,430	1,427,655	n/a
1	Retain small molecules	13,166	1,205,013	n/a
2	Require valid RDKit SMILES	12,303	1,161,857	n/a
3	Restrict to approved compounds	2,643	613,514	n/a
4	Extract DDI edges & normalize descriptions	2,643	613,514	451
5	Remove DDI types with $<$ 10 occurrences	2,151	613,055	221

Appendix

Table 9 : Filtering pipeline statistics. Each step is applied cumulatively.

Label	# DDI Types	# DDI Edges	Edge %
PK	30	295,406	52.2%
PD	184	270,241	47.8%
Mixed (resolved as PK)	1	84	$<$ 0.1%
Total	215	565,731	100%

Appendix

Table 10 : PK/PD classification results. Mixed indicates types where both keyword sets match. In evaluation, Mixed is subsumed under PK via the precedence rule.

Group	# Edges	Edge %	# Unique Key Entities	Dominant Entity
PK-A	167,718	29.6%	79 (enzyme: 38, transporter: 41)	CYP3A4 (48.0% of PK-A)
PK-B	127,772	22.6%	n/a	n/a
PD-A	21,686	3.8%	282 (target: 282)	Histamine H1 receptor (9.7% of PD-A)
PD-B	248,555	43.9%	n/a	n/a

Appendix

Table 11 : A/B subdivision statistics across all 565,731 DDI edges. n/a marks columns inapplicable to Type B by construction (no shared mediating entity).

Table 12

Task	Subset	$n$	Agreement	F1 (pos. 1)	F1 (pos. 2)
PK/PD per-pair	all	500	89.0%	0.884 (PK)	0.895 (PD)
PK/PD type-level	majority-vote types	50	94.0%	0.936 (PK)	0.943 (PD)
A/B per-pair	overall	500	93.8%	0.893 (A)	0.956 (B)

Appendix

Table 14 : Auto-vs-consensus agreement on the final judge-resolved consensus (

N{=}500

). F1 columns report PK and PD as positive class for the PK/PD task, and A and B as positive class for the A/B task.

Task	Subset	$n$	Agreement / $\kappa$
A/B per-quadrant (auto vs. consensus)
A/B per-pair	PK-A	131	77.9%
A/B per-pair	PK-B	119	100.0%
A/B per-pair	PD-A	29	93.1%
A/B per-pair	PD-B	221	100.0%
Inter-annotator (Cohen’s $\kappa$ )

Appendix

Table 15 : A/B per-quadrant breakdown (computed on the automated assignment) and inter-annotator agreement (Cohen’s

\kappa

, computed on the full

N{=}500

paired labels). Per-quadrant agreement equals the precision of the automated label restricted to that quadrant.

Figure 1 : Top-10 DDI types by edge count. Mechanism class encoded by hue (blue = PK, orange = PD), with within-class luminance gradient encoding rank. The top-10 types cover 74.5% of all 565,731 edges. The remaining 205 types are pooled into the grey “Other” slice.

Figure 2 : All 215 DDI types ranked by frequency on a log-scale y-axis. PK types (blue, 31 incl. the 1 Mixed type collapsed to PK per Section A.2 ) concentrate at the high-frequency end while PD types (orange, 184) span a broader, lower-frequency range. The dashed vertical line marks the rank at which cumulative coverage reaches 90% (rank 24).

Figure 3 : Top-30 DDI types on a linear y-axis. Bars are coloured by mechanism class (blue = PK, orange = PD) with raw edge counts annotating the top-10. The dashed horizontal line marks the mean across all 215 types (2,631 edges/type). These 30 types cover 92.3% of the 565,731 edges, reflecting the dominance of a small number of ADME mechanisms.

Path	Contents	Paper anchor
Entry scripts (top level)
reconstruct.py	DrugBank XML $\to$ filtered edges + KG + annotations + splits	Appendices A.1 and B.1
evaluate.py	Conventional baseline training and evaluation	Section 5.1
sanity_check.py	Checks data consistency and reconstruction checksums	Appendix A.6.3
Library package
coldddi/data/filter.py	7-step filtering pipeline	Appendix A.1

Appendix

Table 16 : Release repository layout ( ColdDDI/ ). Top-level entry scripts are immediately runnable. The library code lives under coldddi/ . Paths beginning with coldddi_data/ are generated locally by the walkthrough below.

	Full 1,900-drug			800-drug subset
Quantity	Seed 42	Seed 43	Seed 44	Seed 42	Seed 43	Seed 44
Edge pool sizes
$\mathcal{E}_{\text{all}}^{(G_{1})}$	361,485	366,641	360,330	59,715	62,861	61,554
$\mathcal{E}_{\text{all}}^{(G_{1},G_{2})}$	181,571	177,769	182,380	30,515	29,867	32,448
$\mathcal{E}_{\text{all}}^{(G_{2})}$	22,675	21,321	23,021	3,837	3,450	4,188
Per-split positive counts

Appendix

Table 17 : Per-seed positive edge counts on the full 1,900-drug benchmark and the 800-drug subset across three random seeds. Drug-set sizes are

|G_{1}|/|G_{2}|=1{,}520/380

on the full set and

640/160

on the subset for every seed. Negative edges are sampled at a 1:1 ratio per split ( Section B.2 ) and have identical counts.

Tanimoto cutoff	Train–Test	Train–Train
$>0.85$	$5.18\pm 0.45$	$5.07\pm 0.19$
$>0.90$	$3.77\pm 0.12$	$3.88\pm 0.05$

Appendix

Table 18 : Percentage of drugs whose maximum Tanimoto similarity to the reference set exceeds each cutoff (3-seed mean

\pm

std on the full 1,900-drug benchmark). Train–Test compares held-out drugs against training drugs; Train–Train uses the remaining training drugs as the reference.

Method	1:1	1:3	1:5
DeepDDI	0.678	0.680	0.674
SSI-DDI	0.656	0.646	0.642
DSN-DDI	0.814	0.755	0.793
HDN-DDI	0.675	0.668	0.667
EmerGNN	0.724	0.714	0.706
TIGER	0.581	0.589	0.557

Appendix

Table 19 : S2 AUC under three training-side positive-to-negative ratios (800-drug subset, single random seed). The test set is held fixed at 1:1 across all cells. The

r{=}1

column reuses the default fair bundle.

Method	Random	Hard	Structure-matched
DeepDDI	0.678	0.558	0.672
SSI-DDI	0.656	0.581	0.645
DSN-DDI	0.814	0.718	0.770
HDN-DDI	0.675	0.623	0.621
EmerGNN	0.724	0.619	0.723
TIGER	0.581	0.572	0.569

Appendix

Table 20 : S2 AUC under three training-side negative-sampling strategies (800-drug subset, single random seed). The Random column reuses the default fair bundle.

Statistic	Full (1,900)	Seed 42	Seed 43	Seed 44
Total drugs	1,900	800	800	800
$G_{1}$ (seen)	1,520	640	640	640
$G_{2}$ (unseen)	380	160	160	160
Total positive DDI pairs	565,731	94,067	96,178	98,190
DDI types covered	215	165	152	165
Train positive	325,336	53,743	56,574	55,398

Appendix

Table 21 : Full dataset and 800-drug subsets across the three seeds.

Figure 4 : PK/PD $\times$ A/B proportion in Test S2 across the three seeds on the 800-drug subset. Within the PD class, PD-B dominates over PD-A in every seed, confirming that the mechanism-level composition is stable across the three random seeds.

Method	AUC	Recall	F1
DeepDDI	$0.659\pm 0.018$	$0.486\pm 0.030$	$0.555\pm 0.013$
SSI-DDI	$0.614\pm 0.037$	$0.513\pm 0.116$	$0.548\pm 0.079$
DSN-DDI	$0.758\pm 0.052$	$0.524\pm 0.006$	$0.617\pm 0.019$
HDN-DDI	$0.634\pm 0.035$	$0.633\pm 0.079$	$0.606\pm 0.035$
EmerGNN	$0.716\pm 0.020$	$0.545\pm 0.060$	$0.614\pm 0.039$
TIGER	$0.566\pm 0.014$	$0.473\pm 0.047$	$0.511\pm 0.022$

Appendix

Table 22 : Per-method S2 performance on the 800-drug subset over three random seeds, reported as mean

\pm

standard deviation.

Method	S0	S1	S2	Mean
DeepDDI	0.011	0.005	0.005	0.007
SSI-DDI	0.033	0.021	0.031	0.028
DSN-DDI	0.081	0.141	0.190	0.137
HDN-DDI	0.041	0.007	0.032	0.026
EmerGNN	0.001	0.005	0.009	0.005
TIGER	0.007	0.047	0.053	0.036

Appendix

Table 23 : Per-baseline

|\Delta\mathrm{AUC}|

between the full 1,900-drug benchmark and the 800-drug subset on S0 / S1 / S2 (single random seed). The rightmost column is the mean across the three settings.

Figure 5 : AUROC and Accuracy across subset sizes (100–1,900 drugs) under the three cold-start settings (S0 / S1 / S2). All runs use LoRA-fine-tuned Qwen2.5-0.5B with prompt template P1. The dashed vertical line marks the 800-drug subset used in the main paper.

Drugs	Split	AUROC	AUPRC	Recall	Accuracy	Precision	F1	$n_{\text{pos}}$
100	S0	0.823	0.817	0.630	0.717	0.763	0.690	46
100	S1	0.654	0.620	0.568	0.616	0.628	0.597	241
100	S2	0.541	0.542	0.138	0.500	0.500	0.216	29
200	S0	0.885	0.868	0.801	0.804	0.805	0.803	186
200	S1	0.762	0.735	0.752	0.691	0.670	0.709	975
200	S2	0.654	0.662	0.702	0.621	0.604	0.649	124

Appendix

Table 24 : Full metrics across subset sizes and evaluation settings (Qwen2.5-0.5B, LoRA fine-tuned).

Split	$\boldsymbol{n}$	Top-1	Top-3	Top-5	Macro-F1	Macro-AUROC	Macro-AUPRC
S0	3,069	$0.757_{\pm 0.028}$	$0.943_{\pm 0.017}$	$0.976_{\pm 0.008}$	$0.323_{\pm 0.011}$	$0.995_{\pm 0.001}$	$0.633_{\pm 0.023}$
S1	15,472	$0.517_{\pm 0.030}$	$0.778_{\pm 0.027}$	$0.869_{\pm 0.018}$	$0.207_{\pm 0.016}$	$0.941_{\pm 0.014}$	$0.348_{\pm 0.034}$
S2	1,913	$0.336_{\pm 0.055}$	$0.590_{\pm 0.070}$	$0.706_{\pm 0.062}$	$0.036_{\pm 0.005}$	$0.838_{\pm 0.020}$	$0.192_{\pm 0.044}$

Appendix

Table 25 : Multi-class event-type prediction on the 800-drug subset (3-seed mean

\pm

std on the full S0 / S1 / S2 test sets).

Method	Optim	LR	Weight decay	Batch	Epochs	Patience	Key arch dims	Params	Wall-clock (5090)
									800	1,900
DeepDDI	Adam	$10^{-3}$	$0$	256	100	15	SSP=50, hidden=2048, layers=9, dropout=0.3	29.6M	$\sim$ 10 min	$\sim$ 1 h
SSI-DDI	Adam	$10^{-2}$	$5\times 10^{-4}$	1024	150	50	atom feats=55, KGE dim=64	0.7M	$\sim$ 50 min	$\sim$ 10 h
DSN-DDI	Adam	$10^{-3}$	$5\times 10^{-4}$	512	50	10	hidden=128, KGE dim=128, dropout=0.2	1.4M	$\sim$ 1 h	$\sim$ 14 h
HDN-DDI	Adam	$10^{-3}$	$5\times 10^{-4}$	512	50	10	hidden=128, KGE dim=128	1.6M	$\sim$ 1 h	$\sim$ 14 h
EmerGNN	Adam	$10^{-3}$	$10^{-8}$	32	40	10	$n_{\mathrm{dim}}$ =64, length=3, feat=Morgan FP	0.5M	$\sim$ 40 min	$\sim$ 10 h

Appendix

Table 26 : Hyperparameters used for the eight conventional baselines, matching the paper presets defined in coldddi/baselines/<method>/baseline.py . Wall-clock is approximate end-to-end training on a single RTX 5090 for one run, reported separately for the 800-drug subset and the 1,900-drug full benchmark.

Method	S0		S1		S2
	AUC-ROC	AUC-PRC	AUC-ROC	AUC-PRC	AUC-ROC	AUC-PRC
DeepDDI	0.988 $\pm$ 0.003	0.989 $\pm$ 0.002	0.814 $\pm$ 0.008	0.797 $\pm$ 0.014	0.659 $\pm$ 0.018	0.646 $\pm$ 0.011
SSI-DDI	0.876 $\pm$ 0.026	0.871 $\pm$ 0.027	0.716 $\pm$ 0.015	0.699 $\pm$ 0.011	0.614 $\pm$ 0.037	0.610 $\pm$ 0.025
DSN-DDI	0.981 $\pm$ 0.004	0.980 $\pm$ 0.005	0.876 $\pm$ 0.013	0.876 $\pm$ 0.008	0.758 $\pm$ 0.052	0.747 $\pm$ 0.061
HDN-DDI	0.909 $\pm$ 0.011	0.908 $\pm$ 0.015	0.741 $\pm$ 0.005	0.721 $\pm$ 0.008	0.634 $\pm$ 0.035	0.628 $\pm$ 0.041
EmerGNN	0.982 $\pm$ 0.001	0.984 $\pm$ 0.002	0.805 $\pm$ 0.012	0.793 $\pm$ 0.024	0.716 $\pm$ 0.020	0.701 $\pm$ 0.032

Appendix

Table 27 : Ranking metrics (AUC-ROC, AUC-PRC) for the eight conventional baselines on the 800-drug subset (3-seed mean

\pm

std).

Method	S0		S1		S2
	F1	Acc	F1	Acc	F1	Acc
DeepDDI	0.941 $\pm$ 0.007	0.943 $\pm$ 0.006	0.721 $\pm$ 0.005	0.735 $\pm$ 0.004	0.555 $\pm$ 0.013	0.611 $\pm$ 0.005
SSI-DDI	0.801 $\pm$ 0.025	0.798 $\pm$ 0.026	0.656 $\pm$ 0.004	0.659 $\pm$ 0.012	0.548 $\pm$ 0.079	0.585 $\pm$ 0.037
DSN-DDI	0.929 $\pm$ 0.004	0.928 $\pm$ 0.005	0.764 $\pm$ 0.034	0.784 $\pm$ 0.018	0.617 $\pm$ 0.019	0.674 $\pm$ 0.029
HDN-DDI	0.828 $\pm$ 0.011	0.826 $\pm$ 0.008	0.670 $\pm$ 0.016	0.680 $\pm$ 0.005	0.606 $\pm$ 0.035	0.590 $\pm$ 0.037
EmerGNN	0.932 $\pm$ 0.002	0.933 $\pm$ 0.002	0.714 $\pm$ 0.022	0.735 $\pm$ 0.015	0.614 $\pm$ 0.039	0.659 $\pm$ 0.022

Appendix

Table 28 : Threshold metrics (F1, accuracy) on the 800-drug subset, same runs as Table 27 .

Method	S0		S1		S2
	AUC-ROC	AUC-PRC	AUC-ROC	AUC-PRC	AUC-ROC	AUC-PRC
DeepDDI	0.997 $\pm$ 0.000	0.997 $\pm$ 0.000	0.819 $\pm$ 0.003	0.804 $\pm$ 0.008	0.672 $\pm$ 0.009	0.663 $\pm$ 0.016
SSI-DDI	0.829 $\pm$ 0.010	0.819 $\pm$ 0.013	0.706 $\pm$ 0.006	0.688 $\pm$ 0.006	0.627 $\pm$ 0.003	0.624 $\pm$ 0.014
DSN-DDI	0.950 $\pm$ 0.038	0.947 $\pm$ 0.050	0.827 $\pm$ 0.056	0.818 $\pm$ 0.065	0.711 $\pm$ 0.063	0.696 $\pm$ 0.062
HDN-DDI	0.853 $\pm$ 0.006	0.846 $\pm$ 0.010	0.736 $\pm$ 0.003	0.718 $\pm$ 0.003	0.645 $\pm$ 0.002	0.638 $\pm$ 0.009
EmerGNN	0.980 $\pm$ 0.001	0.982 $\pm$ 0.001	0.799 $\pm$ 0.000	0.776 $\pm$ 0.007	0.707 $\pm$ 0.006	0.680 $\pm$ 0.012

Appendix

Table 29 : Ranking metrics (AUC-ROC, AUC-PRC) on the 1,900-drug full benchmark (3-seed mean

\pm

std).

Method	S0		S1		S2
	F1	Acc	F1	Acc	F1	Acc
DeepDDI	0.973 $\pm$ 0.000	0.973 $\pm$ 0.000	0.726 $\pm$ 0.007	0.743 $\pm$ 0.004	0.575 $\pm$ 0.019	0.624 $\pm$ 0.006
SSI-DDI	0.756 $\pm$ 0.012	0.751 $\pm$ 0.013	0.643 $\pm$ 0.011	0.652 $\pm$ 0.007	0.587 $\pm$ 0.021	0.593 $\pm$ 0.002
DSN-DDI	0.887 $\pm$ 0.055	0.879 $\pm$ 0.064	0.742 $\pm$ 0.041	0.744 $\pm$ 0.059	0.667 $\pm$ 0.076	0.656 $\pm$ 0.061
HDN-DDI	0.775 $\pm$ 0.011	0.772 $\pm$ 0.007	0.669 $\pm$ 0.001	0.675 $\pm$ 0.002	0.597 $\pm$ 0.018	0.603 $\pm$ 0.002
EmerGNN	0.932 $\pm$ 0.004	0.932 $\pm$ 0.004	0.728 $\pm$ 0.002	0.734 $\pm$ 0.001	0.644 $\pm$ 0.011	0.657 $\pm$ 0.007

Appendix

Table 30 : Threshold metrics (F1, accuracy) on the 1,900-drug full benchmark, same runs as Table 29 .

Type	Tier	$n$	DeepDDI	SSI-DDI	DSN-DDI	HDN-DDI	EmerGNN	TIGER	MKG-FENN	TextDDI
PK-A	Low	183	$0.411_{\pm 0.026}$	$0.454_{\pm 0.134}$	$0.464_{\pm 0.053}$	$0.602_{\pm 0.088}$	$\boldsymbol{0.734_{\pm 0.004}}$	$0.373_{\pm 0.097}$	$0.974^{\dagger}_{\pm 0.029}$	$0.565_{\pm 0.051}$
PK-A	Mid	250	$0.497_{\pm 0.074}$	$0.564_{\pm 0.210}$	$0.532_{\pm 0.075}$	$0.720_{\pm 0.055}$	$\boldsymbol{0.772_{\pm 0.058}}$	$0.457_{\pm 0.081}$	$0.994^{\dagger}_{\pm 0.005}$	$0.575_{\pm 0.129}$
PK-A	High	255	$0.605_{\pm 0.042}$	$0.624_{\pm 0.236}$	$0.584_{\pm 0.008}$	$\boldsymbol{0.841_{\pm 0.047}}$	$0.815_{\pm 0.051}$	$0.518_{\pm 0.034}$	$0.994^{\dagger}_{\pm 0.007}$	$0.583_{\pm 0.168}$
PK-B	Low	157	$0.279_{\pm 0.048}$	$0.288_{\pm 0.103}$	$\boldsymbol{0.450_{\pm 0.064}}$	$0.305_{\pm 0.142}$	$0.216_{\pm 0.022}$	$0.357_{\pm 0.045}$	$0.986^{\dagger}_{\pm 0.012}$	$0.392_{\pm 0.058}$
PK-B	Mid	129	$0.374_{\pm 0.025}$	$0.360_{\pm 0.136}$	$0.453_{\pm 0.068}$	$0.460_{\pm 0.180}$	$0.356_{\pm 0.057}$	$0.511_{\pm 0.079}$	$0.991^{\dagger}_{\pm 0.009}$	$\boldsymbol{0.560_{\pm 0.070}}$
PK-B	High	95	$0.511_{\pm 0.050}$	$0.444_{\pm 0.209}$	$0.511_{\pm 0.089}$	$\boldsymbol{0.629_{\pm 0.153}}$	$0.404_{\pm 0.075}$	$0.490_{\pm 0.004}$	$0.990^{\dagger}_{\pm 0.017}$	$0.498_{\pm 0.117}$

Appendix

Table 31 : S2 Recall on the 800-drug subset, broken down by mechanism subtype

\times

Tanimoto similarity tier (3-seed mean

\pm

std). Conventional baselines.

n

is the per-cell positive count averaged across seeds. Best per row in bold (excluding MKG-FENN).

Metric	LLM-FT (P4)			EmerGNN
	C1	C2	C3	C1	C2	C3
Recall	$+0.433$	$+0.433$	$+0.433$	$+0.436$	$+0.436$	$+0.436$
AUC-ROC	$+0.250$	$+0.233$	$+0.296$	$+0.255$	$+0.259$	$+0.266$
AUPRC	$+0.230$	$+0.212$	$+0.195$	$+0.177$	$+0.237$	$+0.154$
MCC	$+0.238$	$+0.405$	$+0.335$	$+0.277$	$+0.437$	$+0.343$
F1	$+0.108$	$+0.300$	$+0.142$	$+0.159$	$+0.357$	$+0.208$

Appendix

Table 32 : Type A–Type B metric gaps under three sampled-unrecorded comparison pools on the 800-drug subset (3-seed mean). Positive values indicate higher scores for Type A under the supplementary shared-mediator labeling rule.

Method	Recall gap	95% CI
DeepDDI	$+0.125_{\pm 0.053}$	$[-0.007,\,+0.258]$
SSI-DDI	$+0.182_{\pm 0.031}$	$[+0.104,\,+0.260]$
DSN-DDI	$+0.067_{\pm 0.063}$	$[-0.089,\,+0.224]$
HDN-DDI	$+0.215_{\pm 0.077}$	$[+0.025,\,+0.405]$
EmerGNN	$+0.388_{\pm 0.078}$	$[+0.194,\,+0.582]$
TIGER	$+0.071_{\pm 0.052}$	$[-0.057,\,+0.199]$

Appendix

Table 33 : S2 A–B Recall gaps (3-seed mean

\pm

std) and pointwise

95\%

confidence intervals on the 800-drug subset, using the labeling protocol of Table 4 .

Added source	Edge increase (%)			Drugs	Recall gap
Added source	Enz.	Tgt.	Trn.	Drugs	LLM-FT	EmerGNN
None	$0.0$	$0.0$	$0.0$	$1{,}793$	$+0.433$	$+0.436$
PharmGKB	$+36.3$	$+12.9$	$+19.8$	$1{,}793$	$+0.432$	$+0.436$
ChEMBL	$+2.1$	$+1.7$	$+1.1$	$1{,}803$	$+0.435$	$+0.436$
KEGG	$+3.2$	$+43.5$	$+1.5$	$1{,}794$	$+0.390$	$+0.392$
All three	$+40.8$	$+57.3$	$+21.7$	$1{,}804$	$+0.395$	$+0.392$

Appendix

Table 34 : Mediator-graph enrichment and A–B Recall gaps. Every row uses DrugBank as the base graph; “All three” adds PharmGKB, ChEMBL, and KEGG. Edge increases and drug coverage refer to the 1,900-drug pool. Recall gaps are 3-seed means on the 800-drug S2 subset. Enz., Tgt., and Trn. denote enzymes, targets, and transporters.

Training-frequency bin	$\boldsymbol{n_{\mathrm{test}}}$	Llama-3.2-1B	Gemma-3-12B	EmerGNN
Shared mediator: $n_{\mathrm{train}}(M)$
$1$ – $10$	$4$	$0.750$	$1.000$	$1.000$
$11$ – $50$	$10$	$0.700$	$0.800$	$0.700$
$51$ – $200$	$14$	$0.786$	$0.643$	$0.571$
$201$ – $1{,}000$	$108$	$0.991$	$0.954$	$0.778$
$>1{,}000$	$2{,}137$	$0.887$	$0.737$	$0.779$

Appendix

Table 35 : Frequency-stratified Recall on the 800-drug S2 subset, pooling test examples across three seeds. The mediator analysis includes Type A positives, and the DDI-type analysis includes all positives.

n_{\mathrm{test}}

denotes the number of test examples in each bin.

Parameter	Search space	Best
learning rate	$[1\!\times\!10^{-5},\;5\!\times\!10^{-4}]$ (step $5\!\times\!10^{-6}$ )	$5\!\times\!10^{-4}$
$(r,\alpha)$	$\{(8,8),\,(8,16),\,(16,16),\,(16,32)\}$	$(16,16)$
dropout	$[0,0.1]$ (step $0.02$ )	$0.1$
target modules	$\{q\!+\!v,\;q\!+\!k\!+\!v,\;q\!+\!k\!+\!v\!+\!o\}$	$q\!+\!k\!+\!v\!+\!o$
batch size	$\{16,\,32,\,64\}$	$16$

Appendix

Table 36 : LoRA hyperparameter search space and the best trial. The Optuna study ran for 50 trials on Qwen2.5-0.5B (1,000 train / 100 val pairs, single random seed), maximising val-S2 AUC-ROC. Best trial value

0.747

, median across all 50 trials

0.506

Parameter	Value
Adapter (PEFT/LoRA)
rank $r$	16
$\alpha$	16
dropout	0.10
target modules	q_proj, k_proj, v_proj, o_proj
layers transformed	11 (last 11 transformer blocks)

Appendix

Table 37 : LoRA adapter and training hyperparameters used for every fine-tuned LLM in the paper. The micro-batch column lists the values used on 2 A40 (96 GB).

Model	P1	P2	P3	P4	P5
Llama-3.2-1B	0.488	0.523	0.465	0.525	0.485
Llama-3.2-3B	0.504	0.488	0.457	0.459	0.468
Llama-2-7B	0.535	0.489	0.549	0.474	0.496
Llama-2-13B	0.545	0.533	0.450	0.513	0.479
Qwen2.5-0.5B	0.525	0.473	0.511	0.489	0.513
Qwen2.5-3B	0.502	0.511	0.555	0.517	0.579

Appendix

Table 38 : S2 AUC-ROC under direct inference (no fine-tuning) on the 800-drug subset (single random seed). Best per column in bold .

Model	P1	P2	P3	P4	P5
Llama-3.2-1B	0.000	0.078	0.850	0.857	0.102
Llama-3.2-3B	0.000	0.003	0.000	0.000	0.000
Llama-2-7B	0.000	0.008	0.000	0.000	0.000
Llama-2-13B	1.000	0.411	0.964	0.995	1.000
Qwen2.5-0.5B	1.000	0.980	1.000	1.000	1.000
Qwen2.5-3B	0.999	0.631	1.000	1.000	0.000

Appendix

Table 39 : S2 Recall under direct inference (no fine-tuning) on the 800-drug subset (single random seed). Best per column in bold .

Model	AUC	Acc.	Prec.	Rec.	F1
GPT-4o	0.710	0.666	0.664	0.672	0.668
Claude Sonnet 4.6	0.751	0.617	0.886	0.267	0.411

Appendix

Table 40 : Closed-source LLM direct inference on the 800-drug subset, S2 test set under P4 (3,838 unique pairs, single random seed). AUC is computed on the elicited

P(\text{Yes})

. Thresholded metrics use

0.5

as the cutoff.

Model	P1	P2	P3	P4	P5
AUC-ROC
Llama-3.2-1B	0.982 $\pm$ 0.003	0.982 $\pm$ 0.005	0.987 $\pm$ 0.002	0.987 $\pm$ 0.001	0.980 $\pm$ 0.003
Llama-3.2-3B	0.987 $\pm$ 0.002	0.965 $\pm$ 0.036	0.946 $\pm$ 0.071	0.991 $\pm$ 0.001	0.984 $\pm$ 0.003
Llama-2-7B	0.883 $\pm$ 0.055	0.975 $\pm$ 0.014	0.962 $\pm$ 0.023	0.893 $\pm$ 0.081	0.967 $\pm$ 0.031
Llama-2-13B	0.891 $\pm$ 0.059	0.930 $\pm$ 0.007	0.915 $\pm$ 0.071	0.903 $\pm$ 0.044	0.907 $\pm$ 0.027
Qwen2.5-0.5B	0.972 $\pm$ 0.003	0.974 $\pm$ 0.003	0.983 $\pm$ 0.004	0.979 $\pm$ 0.007	0.972 $\pm$ 0.004

Appendix

Table 41 : S0 fine-tuned LLM AUC-ROC (top) and Recall (bottom) on the 800-drug subset, by inference pattern (3-seed mean

\pm

std). Best per column in bold .

Model	P1	P2	P3	P4	P5
AUC-ROC
Llama-3.2-1B	0.806 $\pm$ 0.014	0.804 $\pm$ 0.024	0.836 $\pm$ 0.001	0.843 $\pm$ 0.009	0.798 $\pm$ 0.004
Llama-3.2-3B	0.819 $\pm$ 0.021	0.807 $\pm$ 0.012	0.827 $\pm$ 0.010	0.838 $\pm$ 0.014	0.828 $\pm$ 0.005
Llama-2-7B	0.789 $\pm$ 0.025	0.797 $\pm$ 0.006	0.827 $\pm$ 0.008	0.771 $\pm$ 0.067	0.812 $\pm$ 0.011
Llama-2-13B	0.804 $\pm$ 0.017	0.827 $\pm$ 0.010	0.839 $\pm$ 0.024	0.836 $\pm$ 0.019	0.811 $\pm$ 0.007
Qwen2.5-0.5B	0.779 $\pm$ 0.012	0.788 $\pm$ 0.019	0.841 $\pm$ 0.007	0.839 $\pm$ 0.016	0.788 $\pm$ 0.016

Appendix

Table 42 : S1 fine-tuned LLM AUC-ROC (top) and Recall (bottom) on the 800-drug subset (3-seed mean

\pm

std). Best per column in bold .

Model	P1	P2	P3	P4	P5
AUC-ROC
Llama-3.2-1B	0.685 $\pm$ 0.027	0.696 $\pm$ 0.008	0.772 $\pm$ 0.013	0.776 $\pm$ 0.020	0.709 $\pm$ 0.025
Llama-3.2-3B	0.725 $\pm$ 0.030	0.729 $\pm$ 0.025	0.773 $\pm$ 0.004	0.785 $\pm$ 0.018	0.737 $\pm$ 0.023
Llama-2-7B	0.700 $\pm$ 0.015	0.704 $\pm$ 0.009	0.762 $\pm$ 0.010	0.731 $\pm$ 0.039	0.716 $\pm$ 0.023
Llama-2-13B	0.721 $\pm$ 0.022	0.731 $\pm$ 0.005	0.781 $\pm$ 0.017	0.781 $\pm$ 0.015	0.726 $\pm$ 0.017
Qwen2.5-0.5B	0.673 $\pm$ 0.012	0.675 $\pm$ 0.022	0.759 $\pm$ 0.018	0.766 $\pm$ 0.013	0.693 $\pm$ 0.021

Appendix

Table 43 : S2 fine-tuned LLM AUC-ROC (top) and Recall (bottom) on the 800-drug subset (3-seed mean

\pm

std). Best per column in bold .

Setting	AUC-ROC	Accuracy	F1	Precision	Recall
S0	$0.916_{\pm 0.026}$	$0.842_{\pm 0.021}$	$0.841_{\pm 0.021}$	$0.843_{\pm 0.022}$	$0.839_{\pm 0.021}$
S1	$0.806_{\pm 0.038}$	$0.733_{\pm 0.037}$	$0.727_{\pm 0.037}$	$0.745_{\pm 0.041}$	$0.710_{\pm 0.034}$
S2	$0.764_{\pm 0.006}$	$0.701_{\pm 0.007}$	$0.686_{\pm 0.006}$	$0.723_{\pm 0.010}$	$0.652_{\pm 0.004}$

Appendix

Table 44 : Llama-3.2-1B fine-tuned under P4 on the 1,900-drug full benchmark (3-seed mean

\pm

std).

Method / Prompt	AUC-ROC	Recall
TextDDI (RoBERTa)	$0.675_{\pm 0.024}$	$0.590_{\pm 0.059}$
Llama-3.2-1B, P1 (names)	$0.685_{\pm 0.027}$	$0.509_{\pm 0.141}$
Llama-3.2-1B, P7 (names + descriptions)	$0.734_{\pm 0.025}$	$0.681_{\pm 0.062}$
Llama-3.2-1B, P4 (KG context)	$0.776_{\pm 0.020}$	$\mathbf{0.697}_{\pm 0.021}$
Llama-3.2-1B, P6 (KG context + descriptions)	$\mathbf{0.790}_{\pm 0.011}$	$0.691_{\pm 0.042}$

Appendix

Table 45 : Text-description and KG-context comparison on the 800-drug S2 subset (3-seed mean

\pm

std). Best mean per column in bold .

Type	Tier	$n$	Llama-3.2-1B	Llama-3.2-3B	Llama-2-7B	Llama-2-13B
PK-A	Low	183	0.838 $\pm$ 0.113	0.701 $\pm$ 0.191	0.776 $\pm$ 0.157	0.731 $\pm$ 0.119
PK-A	Mid	250	0.908 $\pm$ 0.082	0.716 $\pm$ 0.173	0.818 $\pm$ 0.125	0.731 $\pm$ 0.148
PK-A	High	255	0.911 $\pm$ 0.076	0.782 $\pm$ 0.141	0.829 $\pm$ 0.134	0.740 $\pm$ 0.128
PK-B	Low	157	0.305 $\pm$ 0.108	0.240 $\pm$ 0.049	0.335 $\pm$ 0.079	0.265 $\pm$ 0.055
PK-B	Mid	129	0.433 $\pm$ 0.168	0.352 $\pm$ 0.132	0.436 $\pm$ 0.088	0.351 $\pm$ 0.097
PK-B	High	95	0.511 $\pm$ 0.151	0.379 $\pm$ 0.121	0.506 $\pm$ 0.134	0.429 $\pm$ 0.149

Appendix

Table 46 : S2 Recall on the 800-drug subset, broken down by mechanism subtype

\times

Tanimoto similarity tier. Llama family fine-tuned LLMs under P4 (3-seed mean

\pm

std).

n

is the per-cell positive count averaged across seeds. Best per row in bold .

Type	Tier	$n$	Qwen2.5-0.5B	Qwen2.5-3B	Qwen2.5-7B	Qwen2.5-14B
PK-A	Low	183	0.820 $\pm$ 0.070	0.708 $\pm$ 0.076	0.726 $\pm$ 0.186	0.794 $\pm$ 0.111
PK-A	Mid	250	0.861 $\pm$ 0.072	0.766 $\pm$ 0.104	0.821 $\pm$ 0.121	0.863 $\pm$ 0.055
PK-A	High	255	0.863 $\pm$ 0.054	0.780 $\pm$ 0.073	0.838 $\pm$ 0.099	0.879 $\pm$ 0.054
PK-B	Low	157	0.310 $\pm$ 0.075	0.296 $\pm$ 0.072	0.281 $\pm$ 0.143	0.318 $\pm$ 0.126
PK-B	Mid	129	0.488 $\pm$ 0.054	0.396 $\pm$ 0.069	0.418 $\pm$ 0.150	0.438 $\pm$ 0.138
PK-B	High	95	0.438 $\pm$ 0.016	0.420 $\pm$ 0.100	0.491 $\pm$ 0.189	0.511 $\pm$ 0.138

Appendix

Table 47 : S2 Recall on the 800-drug subset, broken down by mechanism subtype

\times

Tanimoto similarity tier. Qwen family fine-tuned LLMs under P4 (3-seed mean

\pm

std).

n

is the per-cell positive count averaged across seeds. Best per row in bold .

Type	Tier	$n$	Gemma-3-1B	Gemma-3-4B	Gemma-3-12B
PK-A	Low	183	0.643 $\pm$ 0.255	0.753 $\pm$ 0.133	0.669 $\pm$ 0.108
PK-A	Mid	250	0.689 $\pm$ 0.235	0.760 $\pm$ 0.121	0.733 $\pm$ 0.089
PK-A	High	255	0.709 $\pm$ 0.212	0.799 $\pm$ 0.050	0.776 $\pm$ 0.072
PK-B	Low	157	0.262 $\pm$ 0.126	0.275 $\pm$ 0.095	0.236 $\pm$ 0.060
PK-B	Mid	129	0.380 $\pm$ 0.119	0.358 $\pm$ 0.110	0.367 $\pm$ 0.067
PK-B	High	95	0.444 $\pm$ 0.142	0.378 $\pm$ 0.128	0.407 $\pm$ 0.070

Appendix

Table 48 : S2 Recall on the 800-drug subset, broken down by mechanism subtype

\times

Tanimoto similarity tier. Gemma family fine-tuned LLMs under P4 (3-seed mean

\pm

std).

n

is the per-cell positive count averaged across seeds. Best per row in bold .

Setup	$N$	Agreement	$\|\Delta p_{\text{Yes}}\|$	Pearson $r$	AUC ${}_{\text{canon}}$	AUC ${}_{\text{syn}}$
P1 (zero-shot)	556	$0.730\pm 0.037$	$0.229\pm 0.011$	$0.399\pm 0.039$	$0.692\pm 0.008$	$0.638\pm 0.033$
P4 (one-hop KG)	556	$0.846\pm 0.003$	$0.125\pm 0.015$	$0.822\pm 0.016$	$0.771\pm 0.038$	$0.753\pm 0.036$

Appendix

Table 49: Synonym robustness on 800-subset S2 (

n=556

pairs). Mean

\pm

std over 3 seeds. Higher agreement / Pearson and lower

|\Delta p|

indicate name-robustness.

Approval bucket	Drugs	$\bar{n}$ pos	Recall	AUC
Pre-2000	388	1240	$0.687\pm 0.027$	$0.768\pm 0.021$
2000–2009	103	138	$0.801\pm 0.051$	$0.843\pm 0.033$
2010–2019	127	134	$0.864\pm 0.129$	$0.802\pm 0.079$
2020+	62	20	$0.968\pm 0.055$	$0.813\pm 0.084$
Unknown	120	381	$0.543\pm 0.190$	$0.735\pm 0.059$
ALL	800	1913	$0.697\pm 0.021$	$0.776\pm 0.020$

Appendix

Table 50 : Llama-3.2-1B (P4) S2 Recall and AUC, stratified by the older drug’s earliest FDA approval year (3-seed mean

\pm

std on the 800-drug subset). Drugs is the per-bucket drug count on the 800-subset (sums to

800

\bar{n}

is the per-bucket positive pair count averaged across seeds (sums to the S2 positive count).

Cutoff	$\|G_{1}\|/\|G_{2}\|$	$n_{\text{test}}$	AUROC	F1
1985	237 / 443	31,358	$0.687\pm 0.021$	$0.641\pm 0.013$
1995	335 / 345	19,106	$0.717\pm 0.019$	$0.671\pm 0.024$
2005	436 / 244	9,396	$0.753\pm 0.020$	$0.703\pm 0.015$
2010	491 / 189	5,956	$0.760\pm 0.011$	$0.730\pm 0.016$

Appendix

Table 51 : Llama-3.2-1B (P4) on the temporal cold-start splits (3-seed mean

\pm

std).

|G_{1}|/|G_{2}|

is the train / cold-pool drug count after filtering out drugs without a parseable approval year.

ID	Method directory	KG	Drug name	Key entity	Token cap
R0	One_Hop_KG_Sequence	Top-3	real	real	1250
R1	OHKS_Mask_Name	Top-3	[DRUG_*]	real	1250
R2	OHKS_Mask_Entity	Top-3	real	[ENTITY]	1250
R3	OHKS_Mask_Name_Entity	Top-3	[DRUG_*]	[ENTITY]	1250
R4	OHKS_Full	Full	real	real	4096
R5	OHKS_Full_Mask_Name	Full	[DRUG_*]	real	4096

Appendix

Table 52 : Eight masking conditions used in the Section 5.3 factorial. R0–R3 retrieve at most three KG entities per type per drug (Top-3). R4–R7 disable both top-

k

truncation and the prompt-token cap, so every annotated entity is presented to the model.

Bucket	Top-3 KG				Full KG
	R0	R1	R2	R3	R4	R5	R6	R7
PK-A	$0.878_{\pm 0.067}$	$0.893_{\pm 0.055}$	$0.798_{\pm 0.078}$	$0.808_{\pm 0.074}$	$0.890_{\pm 0.051}$	$0.902_{\pm 0.044}$	$0.821_{\pm 0.060}$	$0.829_{\pm 0.058}$
PK-B	$0.602_{\pm 0.079}$	$0.557_{\pm 0.081}$	$0.606_{\pm 0.069}$	$0.553_{\pm 0.060}$	$0.608_{\pm 0.088}$	$0.564_{\pm 0.090}$	$0.612_{\pm 0.083}$	$0.556_{\pm 0.086}$
PD-A	$0.936_{\pm 0.006}$	$0.930_{\pm 0.008}$	$0.907_{\pm 0.028}$	$0.882_{\pm 0.041}$	$0.938_{\pm 0.007}$	$0.935_{\pm 0.008}$	$0.923_{\pm 0.012}$	$0.911_{\pm 0.020}$
PD-B	$0.759_{\pm 0.043}$	$0.725_{\pm 0.035}$	$0.743_{\pm 0.041}$	$0.684_{\pm 0.026}$	$0.758_{\pm 0.043}$	$0.729_{\pm 0.032}$	$0.740_{\pm 0.041}$	$0.689_{\pm 0.021}$
ALL	$0.776_{\pm 0.020}$	$0.759_{\pm 0.014}$	$0.739_{\pm 0.027}$	$0.708_{\pm 0.019}$	$0.781_{\pm 0.021}$	$0.766_{\pm 0.016}$	$0.749_{\pm 0.034}$	$0.720_{\pm 0.026}$

Appendix

Table 53 : AUC-ROC by mechanism bucket × masking condition. 3-seed mean

\pm

std on the 800-drug subset, S2.

Figure 6 : Channel interaction on LLM-FT (800-drug, 3-seed mean, Top-3 KG / R0–R3). Each pair shows one channel’s marginal masking effect with the other channel present (light) vs. already masked (dark). Type A. Modest interaction (KSAI averaged over PK-A and PD-A $=0.025$ ). Type B. Clear cross-channel compensation . Each channel’s marginal effect is larger when the other is already masked (KSAI averaged over PK-B and PD-B $=0.040$ ). Per-bucket KSAI values are reported in Table 55 . Referenced from Section 5.3 .

Method	PK-A	PK-B	PD-A	PD-B	ALL	A–B gap
DeepDDI	$0.333_{\pm 0.081}$	$0.282_{\pm 0.065}$	$0.371_{\pm 0.070}$	$0.318_{\pm 0.056}$	$0.317_{\pm 0.066}$	$+0.052_{\pm 0.030}$
SSI-DDI	$0.166_{\pm 0.058}$	$0.150_{\pm 0.060}$	$0.195_{\pm 0.037}$	$0.166_{\pm 0.055}$	$0.164_{\pm 0.056}$	$+0.022_{\pm 0.014}$
DSN-DDI	$0.420_{\pm 0.068}$	$0.384_{\pm 0.074}$	$0.451_{\pm 0.050}$	$0.416_{\pm 0.063}$	$0.412_{\pm 0.065}$	$+0.035_{\pm 0.021}$
HDN-DDI	$0.164_{\pm 0.047}$	$0.133_{\pm 0.042}$	$0.186_{\pm 0.056}$	$0.153_{\pm 0.040}$	$0.154_{\pm 0.043}$	$+0.032_{\pm 0.010}$
EmerGNN	$\boldsymbol{0.486_{\pm 0.019}}$	$\boldsymbol{0.294_{\pm 0.012}}$	$\boldsymbol{0.492_{\pm 0.087}}$	$\boldsymbol{0.343_{\pm 0.041}}$	$\boldsymbol{0.388_{\pm 0.016}}$	$\boldsymbol{+0.170_{\pm 0.057}}$
TIGER	$0.213_{\pm 0.067}$	$0.218_{\pm 0.074}$	$0.237_{\pm 0.092}$	$0.216_{\pm 0.078}$	$0.216_{\pm 0.074}$	$+0.008_{\pm 0.018}$

Appendix

Table 54 : KPS-F per (method, bucket), 3-seed mean

\pm

std. Bold marks the two methods with substantial A–B gaps. Sample counts

|\mathcal{C}_{b}|

aggregated across seeds. ALL

\!\approx\!230

K, PK-A

\!\approx\!81

K, PK-B

\!\approx\!47

K, PD-A

\!\approx\!8.4

K, PD-B

\!\approx\!97

Indicator	PK-A	PK-B	PD-A	PD-B	ALL
KPS-Name (KG present)	$0.083_{\pm 0.028}$	$0.133_{\pm 0.033}$	$0.069_{\pm 0.005}$	$0.129_{\pm 0.034}$	$0.112_{\pm 0.028}$
KPS-KG (Name present)	$0.167_{\pm 0.022}$	$0.106_{\pm 0.009}$	$0.090_{\pm 0.036}$	$0.121_{\pm 0.009}$	$0.134_{\pm 0.009}$
KPS-KG (Name masked)	$0.179_{\pm 0.020}$	$0.139_{\pm 0.006}$	$0.128_{\pm 0.028}$	$0.168_{\pm 0.008}$	$0.165_{\pm 0.007}$
KSAI-masking	$0.012_{\pm 0.010}$	$0.033_{\pm 0.005}$	$0.038_{\pm 0.008}$	$0.046_{\pm 0.004}$	$0.031_{\pm 0.004}$

Appendix

Table 55 : LLM-FT (Llama-3.2-1B, P4) per-bucket channel-mask indicators. KPS-KG and KPS-Name are mask-derived (R0/R1/R2/R3 of Table 52 ). KSAI-masking is the

2\!\times\!2

factorial interaction

|P_{R_{1}}\!-\!P_{R_{3}}|\!-\!|P_{R_{0}}\!-\!P_{R_{2}}|

. 3-seed mean

\pm

std.

Method	Channel	PK-A	PK-B	PD-A	PD-B	ALL
MKG-FENN	KPS-mol	$0.234_{\pm 0.016}$	$0.242_{\pm 0.019}$	$0.132_{\pm 0.023}$	$0.246_{\pm 0.035}$	$0.235_{\pm 0.011}$
MKG-FENN	KPS-KG	$0.294_{\pm 0.045}$	$0.317_{\pm 0.056}$	$0.297_{\pm 0.067}$	$0.321_{\pm 0.039}$	$0.309_{\pm 0.040}$
TIGER	KPS-mol	$0.102_{\pm 0.046}$	$0.096_{\pm 0.040}$	$0.103_{\pm 0.058}$	$0.099_{\pm 0.045}$	$0.099_{\pm 0.044}$
TIGER	KPS-KG	$0.209_{\pm 0.068}$	$0.217_{\pm 0.088}$	$0.199_{\pm 0.075}$	$0.208_{\pm 0.075}$	$0.209_{\pm 0.076}$

Appendix

Table 56 : Channel-mask KPS for the two multi-channel baselines (MKG-FENN, TIGER) on the 800-drug subset, S2, 3-seed mean

\pm

std. Mask is symmetric (both drugs blanked). See Appendix E.1 for the per-method embedding-zero protocol.

Function	Head	KG attn	Name attn	Layer
KG-focused	L7 H3	60.1%	12.5%	Mid (7)
KG-focused	L7 H8	50.8%	14.5%	Mid (7)
KG-focused	L7 H11	53.5%	17.2%	Mid (7)
Name-focused	L14 H21	18.7%	70.4%	Deep (14)
Name-focused	L13 H5	24.5%	33.0%	Deep (13)
Name-focused	L10 H23	21.9%	17.1%	Mid-deep (10)

Appendix

Table 57 : Top specialised attention heads in the fine-tuned Llama-3.2-1B identified by token-class attention fraction.

Panel of Figure 7 — Figure 7 : Fine-tuning redistributes attention from instruction tokens to KG entities and drug names, and concentrates this gain in three KG-attending heads at layer 7 and three name-attending heads at layers 10–15.

Condition	ALL	PK-A	PK-B	PD-A	PD-B
Baseline	0.798	0.939	0.690	0.930	0.733
Suppress KG heads (3)	0.776	0.920	0.689	0.875	0.702
Suppress Name heads (3)	0.799	0.948	0.685	0.937	0.730
Suppress ALL KG	0.673	0.720	0.630	0.739	0.652
Suppress ALL Name	0.774	0.939	0.650	0.928	0.697
Suppress L7 H3 only	0.786	0.945	0.690	0.898	0.704

Appendix

Table 58 : Attention intervention on LLM-FT (S2). The top block reports absolute AUROC, and the bottom block reports

\Delta

AUROC relative to the unintervened baseline. Bold marks the largest effect per group. Suppressing all KG attention collapses PK-A by

-0.219

, while suppressing all Name attention hurts PD-B (

-0.036

) but leaves PK-A unchanged (

+0.000

Panel of Figure 8 — Figure 8 : Controlled attention intervention. (top) $\Delta$ AUROC across DDI groups under five intervention conditions plus the unintervened baseline. (bottom) Comparison between attention-level intervention and input-level token masking from Section 5.3 .

Cat.	Method	Best lr	ALL	PK-A	PK-B	PD-A	PD-B	$\boldsymbol{\Delta}$ ALL
—	Baseline (LoRA-FT)	—	0.798	0.939	0.690	0.930	0.733	—
A	SAB	5e-4	0.799	0.934	0.688	0.935	0.739	$+$ 0.1
A	PHT	1e-3	0.797	0.927	0.686	0.931	0.739	$-$ 0.2
A	THB	1e-3	0.799	0.939	0.690	0.930	0.734	$+$ 0.0
A	ISB	1e-2	0.799	0.937	0.687	0.935	0.737	$+$ 0.1
B	IAD ( $\gamma{=}0.8$ )	—	0.796	0.939	0.688	0.925	0.730	$-$ 0.2

Appendix

Table 59 : Attention-steering results on S2 AUROC, single random seed on the 800-drug subset.

\Delta

ALL is reported in

\times 10^{2}

relative to the LoRA-FT baseline (

0.798

, single seed). No method achieves a meaningful improvement.

Condition	ALL	PK-A	PK-B	PD-A	PD-B
Llama-3.2-1B: AUROC
Baseline	$0.776_{\pm 0.020}$	$0.878_{\pm 0.068}$	$0.602_{\pm 0.078}$	$0.949_{\pm 0.017}$	$0.759_{\pm 0.043}$
Suppress KG heads (3)	$0.754_{\pm 0.019}$	$0.857_{\pm 0.066}$	$0.609_{\pm 0.069}$	$0.880_{\pm 0.051}$	$0.726_{\pm 0.044}$
Suppress Name heads (3)	$0.778_{\pm 0.018}$	$0.889_{\pm 0.063}$	$0.596_{\pm 0.079}$	$0.957_{\pm 0.017}$	$0.758_{\pm 0.041}$
Suppress ALL KG	$0.661_{\pm 0.011}$	$0.670_{\pm 0.049}$	$0.586_{\pm 0.039}$	$0.758_{\pm 0.018}$	$0.685_{\pm 0.036}$
Suppress ALL Name	$0.759_{\pm 0.015}$	$0.894_{\pm 0.054}$	$0.556_{\pm 0.082}$	$0.950_{\pm 0.019}$	$0.725_{\pm 0.033}$

Appendix

Table 60 : Attention-intervention stability on S2 of the 800-drug subset (3-seed mean

\pm

std). For each model, the upper block reports absolute AUROC and the lower block reports within-seed

\Delta

AUROC relative to the unintervened baseline.

Figure 9 : S2 pair embeddings projected onto each method’s Fisher LDA axis and residual PC1. Rows. Without/with one-hop KG. Columns. TIGER, LLM-FT. Red/blue dots. PK/PD positives, full opacity if predicted correctly and half opacity otherwise. Dashed line. 2D logistic regression on the model’s predicted label, fitted on pooled (no-KG, with-KG) pairs and shared between the two rows of a column. Dashed ellipses. $2\sigma$ Gaussian confidence regions per class on the visible subset. $\times$ markers. Per-class centroids on the visible subset. The KG-induced centroid shift can be read by comparing the $\times$ markers between the two rows of a column. Bottom-right inset (bottom row). FN $\to$ TP and TP $\to$ FN counts split by PK/PD plus signed net total. Net flip . $-114$ (TIGER, KG hurts), $+234$ (LLM-FT, KG helps).

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